INTERPRETATION, INTERACTION, AND SCALABILITY FOR STRUCTURAL DECOMPOSITION TREES

René Rosenbaum, Daniel Engel, James Mouradian, Hans Hagen, Bernd Hamann

2012

Abstract

Structural Decomposition Trees (SDTs) have been proposed as a completely novel display approach to tackling the research problem of visualizing high-dimensional data. SDTs merge the two distinct classes of relation and value visualizations into a single integrated strategy. The method is promising; however, statements regarding its meaningful application are still missing, constraining its broad adoption. This paper introduces solutions for still-existing issues in the application of SDTs with regard to interpretation, interaction, and scalability. SDTs provide a well-designed initial projection of the data to meaningfully represent its properties, but not much is known about how to interpret this projection. We are able to derive the data’s properties from their initial representation. The provided methods are valid not only for SDTs, but also for projections based on principal components analysis, addressing a frequent problem when applying this technology. We further show how interactive exploration based on SDTs can be applied to visual cluster analysis as one of its application domains. To address the urgent need to analyze vast and complex amounts of data, we also introduce means for scalable processing and representation. Given the importance and broader relevance of the discussed problem domains, this paper justifies and further motivates the usefulness and wide applicability of SDTs as a novel visualization approach for high-dimensional data.

References

  1. Ankerst, M., Berchtold, S., and Keim, D. A. (1998). Similarity clustering of dimensions for an enhanced visualization of multidimensional data. In INFOVIS 7898: Proceedings of the 1998 IEEE Symposium on Information Visualization, pages 52-60, Washington, DC, USA. IEEE Computer Society.
  2. Artero, A. O., de Oliveira, M. C. F., and Levkowitz, H. (2004). Uncovering clusters in crowded parallel coordinates visualizations. In Proceedings of the IEEE Symposium on Information Visualization, pages 81- 88, Washington, DC, USA. IEEE Computer Society.
  3. Bein, K., Zhao, Y., and Wexler, A. (2009). Conditional sampling for source-oriented toxicological studies using a single particle mass spectrometer. Environmental Sciience and Technology, 43(24):9445-9452.
  4. Ellis, G. and Dix, A. (2007). A taxonomy of clutter reduction for information visualisation. IEEE Transactions on Visualization and Computer Graphics, 13(6):1216-1223.
  5. Elmqvist, N., Dragicevic, P., and Fekete, J.-D. (2008). Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation. IEEE Transactions on Visualization and Computer Graphics, 14:1141-1148.
  6. Engel, D., Rosenbaum, R., Hamann, B., and Hagen, H. (2011). Structural decomposition trees. Computer Graphics Forum, 30(3):921-930.
  7. Hauser, H., Ledermann, F., and Doleisch, H. (2002). Angular brushing of extended parallel coordinates. In INFOVIS 7802: Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02), pages 127- 130, Washington, DC, USA. IEEE Computer Society.
  8. Hoffman, P., Grinstein, G., and Pinkney, D. (1999). Dimensional anchors: a graphic primitive for multidimensional multivariate information visualizations. In NPIVM 7899: Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation, pages 9-16, New York, NY, USA. ACM.
  9. Ingram, S., Munzner, T., and Olano, M. (2009). Glimmer: Multilevel mds on the gpu. IEEE Transactions on Visualization and Computer Graphics, 15:249-261.
  10. Jänicke, H., Böttinger, M., and Scheuermann, G. (2008). Brushing of attribute clouds for the visualization of multivariate data. IEEE Transactions on Visualization and Computer Graphics, 14:1459-1466.
  11. Johansson, J., Ljung, P., Jern, M., and Cooper, M. (2005). Revealing structure within clustered parallel coordinates displays. In Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization, pages 125-132, Washington, DC, USA. IEEE Computer Society.
  12. Johansson, S. and Johansson, J. (2009). Interactive dimensionality reduction through user-defined combinations of quality metrics. IEEE Transactions on Visualization and Computer Graphics, 15:993-1000.
  13. Kandogan, E. (2001). Visualizing multi-dimensional clusters, trends, and outliers using star coordinates. In Proceedings of the ACM international conference on Knowledge discovery and data mining, pages 107- 116, New York, NY, USA. ACM.
  14. Lorensen, W. E. and Cline, H. E. (1987). Marching cubes: A high resolution 3d surface construction algorithm. Computer Graphics, 21(4):163-169.
  15. McDonnell, K. T. and Mueller, K. (2008). Illustrative parallel coordinates. Computer Graphics Forum, 27(3):1031-1038.
  16. Oesterling, P., Heine, C., Jänicke, H., and Scheuermann, G. (2010). Visual analysis of high dimensional point clouds using topological landscapes. In Pacific Visualization Symposium (PacificVis), 2010 IEEE, pages 113 -120.
  17. Paulovich, F. V., Oliveira, M. C. F., and Minghim, R. (2007). The projection explorer: A flexible tool for projection-based multidimensional visualization. In Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing, pages 27-36, Washington, DC, USA. IEEE Computer Society.
  18. Peng, W., Ward, M. O., and Rundensteiner, E. A. (2004). Clutter reduction in Multi-Dimensional data visualization using dimension reordering. In Proceedings of the IEEE Symposium on Information Visualization, pages 89-96, Washington, DC, USA. IEEE Computer Society.
  19. Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. Proceedings of the IEEE Symposium on Visual Languages, pages 336-343.
  20. Yang, J., Patro, A., Huang, S., Mehta, N., Ward, M. O., and Rundensteiner, E. A. (2004). Value and relation display for interactive exploration of high dimensional datasets. In Proceedings of the IEEE Symposium on Information Visualization, pages 73-80, Washington, DC, USA. IEEE Computer Society.
  21. Yang, J., Peng, W., Ward, M. O., and Rundensteiner, E. A. (2003a). Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets. In Proceedings of the Ninth annual IEEE conference on Information visualization, pages 105-112, Washington, DC, USA. IEEE Computer Society.
  22. Yang, J., Ward, M. O., Rundensteiner, E. A., and Huang, S. (2003b). Visual hierarchical dimension reduction for exploration of high dimensional datasets. In Proceedings of the symposium on Data visualisation 2003, VISSYM 7803, pages 19-28, Aire-la-Ville, Switzerland, Switzerland. Eurographics Association.
  23. Yang, J., Ward, M. O., Rundensteiner, E. A., and Huang, S. (2003c). Visual hierarchical dimension reduction for exploration of high dimensional datasets. In Proceedings of the Symposium on Data visualisation 2003, VISSYM 7803, pages 19-28, Aire-la-Ville, Switzerland, Switzerland. Eurographics Association.
  24. Yuan, X., Guo, P., Xiao, H., Zhou, H., and Qu, H. (2009). Scattering points in parallel coordinates. IEEE Transactions on Visualization and Computer Graphics, 15:1001-1008.
  25. Zhou, H., Yuan, X., Qu, H., Cui, W., and Chen, B. (2008). Visual clustering in parallel coordinates. Computer Graphics Forum, 27(3):1047-1054.
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Paper Citation


in Harvard Style

Rosenbaum R., Engel D., Mouradian J., Hagen H. and Hamann B. (2012). INTERPRETATION, INTERACTION, AND SCALABILITY FOR STRUCTURAL DECOMPOSITION TREES . In Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2012) ISBN 978-989-8565-02-0, pages 636-647. DOI: 10.5220/0003841106360647


in Bibtex Style

@conference{ivapp12,
author={René Rosenbaum and Daniel Engel and James Mouradian and Hans Hagen and Bernd Hamann},
title={INTERPRETATION, INTERACTION, AND SCALABILITY FOR STRUCTURAL DECOMPOSITION TREES},
booktitle={Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2012)},
year={2012},
pages={636-647},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003841106360647},
isbn={978-989-8565-02-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2012)
TI - INTERPRETATION, INTERACTION, AND SCALABILITY FOR STRUCTURAL DECOMPOSITION TREES
SN - 978-989-8565-02-0
AU - Rosenbaum R.
AU - Engel D.
AU - Mouradian J.
AU - Hagen H.
AU - Hamann B.
PY - 2012
SP - 636
EP - 647
DO - 10.5220/0003841106360647